Tech Transformed

In the automotive industry, trust and transparency are no longer optional; they have become key components. Dealerships that communicate clearly and responsibly with their customers strengthen relationships and improve overall experiences. In this episode of Tech Transformed, host Trisha Pillay speaks with Sean Barrett, Chief Information Officer at CallRevu, about how dealerships can navigate the changing landscape of communication while maintaining accountability, compliance and operational resilience.

Communication has always been at the heart of dealership operations. The phone system was once the primary lifeline between customers and dealerships, giving managers the visibility needed to ensure interactions were handled correctly. Today, communication extends far beyond the phone. SMS, MMS, instant messaging, and other channels allow customers to engage in multiple ways.

Sean explains how integrating these channels into a single technology platform provides managers with a clear view of all interactions, ensuring employees follow policies and customers receive the attention they deserve. This approach strengthens trust and improves the overall customer experience.

Alongside multi-channel communication, compliance and data privacy are critical. Regulations like GDPR and UN R155 require dealerships to protect customer data while maintaining seamless communication. Transparent practices, combined with adherence to regional rules, help build trust and protect both customers and the dealership’s reputation. Observing patterns in customer interactions also allows dealerships to make informed decisions, improve processes, and enhance service quality. Using these data insights, dealerships can make communication more effective and meaningful for every customer.

Reliable infrastructure underpins all communication efforts. Sean shares how dealerships can prepare for unexpected disruptions with geo-redundant systems, cloud-based platforms, and layered internet backups, including options like Starlink or fibre connections. These measures ensure dealerships stay operational, customers can reach them without interruption, and business continuity is maintained.

As new channels emerge, proactive preparation is key. Dealerships that view communication as an investment, rather than a cost, position themselves for long-term success. Monitoring trends, adapting quickly, and fostering transparency help maintain strong customer relationships even as expectations evolve.

Staff development is a critical component of a communication strategy. By using insights from technology platforms, dealerships can guide employee training, build accountability, and create a culture of learning. Confident, well-trained teams contribute to consistent, high-quality interactions that enhance customer trust.

Success in automotive communication isn’t just about adopting the latest tools—it’s about building systems and practices that protect customers, support employees, and foster trust at every touchpoint. Sean Barrett’s insights provide a roadmap for dealerships aiming to elevate communication strategies, improve customer satisfaction, and maintain resilience in an increasingly connected world.

Learn more: callrevu.com

Takeaways

1. Trust and transparency are essential for customer relationships.
2. The evolution of communication systems has introduced multi-channel interactions.
3. Compliance challenges vary across regions and regulations.
4. AI can provide deeper insights into customer interactions.
5. Training with AI can enhance employee performance and accountability.
6. Dealerships should view communication as an investment, not an expense.
7. Data insights are crucial for improving customer experience.
8. Emerging communication channels require proactive preparation.
9. A risk-free training environment can foster employee growth.
10. Customer lifetime value is realised through effective data utilisation.

00:00 Introduction to Trust and Transparency in Automotive Communication

03:06 Evolution of Dealership Communication Systems

05:02 Building Reliable Communication Infrastructure

07:13 Navigating Compliance Challenges in Automotive

09:12 Preparing for Emerging Communication Channels

11:15 The Role of AI in Automotive Communication

14:10 Bridging the Gap Between AI and Human Interaction

16:35 Success Stories and Training Innovations

18:22 Practical Actions for Dealerships

22:14 Conclusion and Key Takeaways

CallRevu is the leading communication intelligence platform built for automotive retail, empowering dealerships to take control of every conversation, from the first ring to the final result. Its unified solution combines a hosted phone system, call monitoring, performance training, and reputation management fueled by AI-powered analytics that turn every customer interaction into actionable intelligence.

Founded in a dealership in 2008, CallRevu was created by the industry, for the industry. We deliver the tools dealerships need to drive revenue, improve operations, and deliver exceptional customer experiences.

In a world where customer expectations evolve faster than ever, organisations are rethinking how they manage and leverage data. Legacy, monolithic Customer Data Platforms (CDPs) are increasingly challenged by rigidity, slow adaptability, and regulatory pressures. In this episode of Tech Transformed, Christina Stathopoulos, Founder of Dare to Data, speaks with Joe Pulickal, Director of Product Management at Uniphore, about the shift to composable CDPs and what it means for modern marketing technology.

Organisations are moving away from rigid, all-in-one CDPs as regulations around data privacy, consent, and cross-border data flows intensify. Joe explains that companies can no longer rely on systems that lock them into a single architecture or make compliance retrofitting difficult. Data governance, consent management, and data sovereignty have become critical considerations in every technology decision, forcing leaders to rethink the underlying structure of their CDPs.

While composable CDPs offer flexibility, they introduce new challenges. Organisations must define ownership and accountability within modular systems to prevent fragmentation and ensure consistent data quality. Leadership must consider how compute, storage, and access are distributed across modules while maintaining compliance and security standards. Joe notes that without clarity on ownership, organisations risk operational inefficiency and weakened governance.

The core advantage of composable architectures lies in modularity. By decoupling components from data ingestion to activation, organisations gain the freedom to innovate without being constrained by a monolithic platform. Joe emphasises: “You need flexibility in where data lives, how compute happens, ultimately doubling down on sovereignty, security, and that composable idea that initially started with data.” This approach allows teams to adopt new tools, scale selectively, and respond to changing business or regulatory requirements with agility.

The shift to first-party data strategies is essential in today’s marketing landscape. With third-party cookies being phased out and privacy regulations tightening, companies must rely on direct, trusted data from their customers. Composable CDPs provide the framework to centralise first-party data while giving teams the ability to personalise experiences, maintain compliance, and safeguard trust. Joe highlights that organisations need to view data not just as an asset, but as a responsibility, balancing customer value with ethical management.

Here are what leaders can do:

1. Rethink data architecture: Move from monolithic to composable systems to gain flexibility, scalability, and regulatory alignment.
2. Prioritise governance: Define ownership, consent management, and security practices across modular components.
3. Focus on first-party data: Build direct customer relationships and leverage trusted data responsibly.
4. Embrace modularity: Enable innovation, adaptability, and resilience in data management through composable design.

This episode offers practical insights for leaders navigating the transition from traditional CDPs to composable architectures. It highlights how thoughtful design, governance, and first-party data strategies empower organisations to act with agility, comply with regulations, and deliver better customer experiences.

For more information, book a demo with Uniphore.

• Organisations are moving away from rigid monolithic CDPs due to regulatory pressures.
• Composable architectures offer flexibility and modularity in data management.
• The shift to first-party data strategies is essential in the current landscape.
• Data governance and consent management are critical in modern marketing.
• Organisations face challenges in defining ownership within composable systems.
• Uniphore supports hybrid deployment options for data management.
• AI integration is crucial for enhancing CDP functionalities.
• Flexibility in architecture helps avoid vendor lock-in.
• People and processes are as important as technology in CDP implementation.
• Clear alignment on objectives is necessary for successful transitions.

00:00 The Shift from Monolithic CDPs to Composable Architectures

08:18 Understanding the Limitations of Monolithic CDPs

10:59 Rethinking First-Party Data Strategies

17:44 Challenges in Implementing Composable CDPs

21:18 Uniphore Role in Composable Marketing Intelligence

25:28 Future Considerations for CDP Ecosystems

Uniphore is a B2B artificial intelligence (AI) company that provides a full-stack Business AI Cloud platform for enterprises to manage customer interactions, sales, marketing, and internal operations. Founded in 2008 and incubated at IIT Madras, Uniphore has dual headquarters in Palo Alto, California, and Chennai, India, and reached unicorn status with a $2.5 billion valuation in 2022. The platform is designed to be sovereign, composable, and secure, allowing businesses to connect diverse data sources, leverage AI models, and deploy AI agents across the enterprise. Uniphore’s offerings include Customer Service AI for agent guidance and analytics, Sales AI for real-time insights, Marketing AI with CDP capabilities, and People AI for HR automation.

As companies rethink how they provide customer experiences (CX), a new form of AI capability, agentic AI, is quickly changing how work is accomplished in contact centres. 

In the recent episode of the Tech Transformed podcast, Dialpad Lead Product Manager Calvin Hohener sits down with host Jon Arnold, Principal at J Arnold & Associates. They discuss the transition from legacy chatbots to more autonomous agents capable of completing tasks and improving customer interactions.

The conversation highlights the importance of understanding the technology's impact on enterprise architecture, the need for clean data, and the strategic implications for C-level executives. Hohener emphasises the importance of starting with clear use cases and working closely with vendors to maximise the potential of AI in business operations.

Most people have used chatbots and found them lacking. Hohener explains why: earlier conversational AI was based on retrieval-augmented generation (RAG). These systems could take user input, search a knowledge base or the internet, and provide an answer. This was helpful for customer service queries, but limited.

“Previous AI models could retrieve and return information, but now we’re moving into a new phase with agentic AI.” Agentic AI can take action rather than just providing information. 

For AI agents to succeed, organisations must first organise their data. “How your internal knowledge is structured is crucial. Even if the data is unorganised, you need to know its location and ensure it’s clean,” stated Hohener.

Agentic systems depend on internal knowledge, including knowledge base articles, CRM notes, and process documentation. If this foundation is disordered, the agent’s output will not be reliable. 

This isn’t about achieving ideal data cleanliness from the start; it’s about knowing what information exists, where it is, and whether it can be trusted. If an AI agent bases its decisions on outdated, conflicting, or incomplete content, it will struggle to perform tasks aptly, regardless of how sophisticated the model is. Enterprises need at least basic clarity about which systems hold which knowledge, who is responsible for them, and whether there is consistency across sources.

Hohener noted that organisations often overlook how quickly conflicting information can undermine an otherwise well-designed agent. A single outdated procedure or mismatched policy in a knowledge repository can lead an AI to produce incorrect results or halt during workflow execution. 

Keeping internal content clean, deduplicated, and consistent gives the agent a reliable, valid source. This reliability becomes crucial when AI starts taking meaningful actions, not just providing answers.

By focusing on data readiness early, enterprises not only reduce deployment obstacles but also set the stage for scaling agentic AI across more complex processes. In many ways, preparing data isn’t just a technical task; it’s an organisational one. 

The Dialpad Lead Product Manager noted that human roles, too, will evolve with agentic AI entering the contact centre. For instance, human agents will take on more of an advisory role—reviewing conversation traces and helping adjust the models.”

Instead of just resolving customer issues, they will help refine and oversee AI workflows. This includes reviewing conversation logs, noting where an AI agent may have misinterpreted intent, and providing feedback to improve the models.

This mentor-mentee relationship between human and digital agents becomes crucial as organisations increase automation. Human agents bring domain knowledge, contextual judgment, and the ability to handle unique situations, all of which help the AI improve over time. In return, digital agents reduce the repetitive workload and allow humans to engage in higher-level thinking. 

• The time is now for adopting agentic AI solutions.
• Agentic AI represents a significant shift in customer experience technology.
• Legacy chatbots have limitations that agentic AI can overcome.
• Real-world applications of agentic AI include mundane tasks like scheduling and verification.
• Quantifying time savings is crucial for measuring ROI with AI.
• AI agents can improve customer satisfaction metrics when implemented correctly.
• Data organisation is essential for effective AI deployment.
• Human agents will focus on more complex cases as AI handles routine tasks.
• C-level executives should view AI as a strategic investment.
• Collaboration with reputable vendors is key to successful AI implementation.

• 00:00 Introduction to Agentic AI
• 05:10 Evolution Beyond Legacy Chatbots
• 11:44 Real-World Applications of Agentic AI
• 15:38 Impact on Enterprise Architecture
• 21:41 Strategic Considerations for C-Level Executives

Client service teams are at a breaking point. Margins are shrinking, the demand keeps rising, and much of the day is consumed by work that doesn’t move the needle. As a result, skilled people often spend hours reconciling spreadsheets, re-entering the same data across multiple systems, and chasing updates, time that should be spent on the work clients actually pay for. Every hour lost to manual admin is an hour of revenue slipping away. In this day and age, that’s a hit no business can afford.

AI isn’t just a buzzword here; it’s a practical lever. It can cut through the repetitive tasks that slow teams down, surface the information they need instantly, and free them to focus on high-value work. The companies winning aren’t replacing staff; they’re removing the obstacles that keep people from doing their best. In a world where speed and accuracy matter more than ever, ignoring that shift isn’t optional.

In the latest episode of Tech Transformed, hosted by Christina Stathopolus, founder of Dare to Data, Daniel Mackey, CEO of Teamwork.com, discussed how AI is reshaping the daily operations of client service teams. From automating repetitive admin tasks to surfacing critical information faster, AI is giving teams the bandwidth to focus on the work that truly drives value for clients. 

During the conversation, Mackey highlighted how AI is reshaping business operations, emphasising efficiency and productivity rather than job displacement. “AI has transformed our company,” he noted, pointing to tangible improvements across workflow and project management. Teams are now able to focus on strategic initiatives, leaving repetitive tasks to intelligent systems.

The Teamwork.com CEO also shared a recent example from a government agency that integrated AI into its processes. By automating routine administrative work, the agency experienced better resource allocation and improved project outcomes. “They’re more efficient, higher quality,” Mackey said. “AI allows them to focus on the bigger parts of the business.”

One of the challenges in the industry is that most AI features are added onto existing tools that weren’t designed for client services. Mackey discussed how TeamworkAI addresses this gap. Built into a platform designed specifically for managing client services end-to-end, TeamworkAI connects projects, people, and profits in one system.

By integrating AI directly into client delivery workflows, organisations can streamline project management, reduce manual reporting, and ensure that technology enhances rather than disrupts service delivery. This approach allows businesses to use technology strategically, rather than simply automating isolated tasks.

The discussion also touched on the broader impact of AI on traditional business models. Organisations that adopt AI thoughtfully can improve their internal processes, freeing employees from repetitive tasks and enabling them to contribute to higher-value projects. Mackey emphasised that the goal isn’t just automation, it’s profitable client delivery. AI can unlock both time and insight, allowing businesses to prioritise the most impactful work.

AI is redefining how businesses allocate resources, manage projects, and deliver value to clients. By eliminating repetitive work and connecting projects, people, and profits, technologies like TeamworkAI show how AI can drive efficiency, productivity, and business transformation without sacrificing the human element.

To discover how your team can leverage TeamworkAI to deliver seamless projects, smarter resourcing, and stronger profits, visit teamwork.com

• Profitable client delivery now depends on AI – learn what actually works before investing in the wrong tools.
• Efficiency gains from AI lead directly to better performance, improving output and reducing wasted effort.
• AI and automation free up resources, allowing organisations to focus on strategic priorities.
• Technology adoption reshapes business models, ensuring organisations remain competitive in a rapidly changing landscape.
• AI integration is critical for competitiveness; without it, margins and business outcomes could be at risk.

-00:00 Introduction to Client Services and AI

- 3:00 The Role of AI in Streamlining Workflows

- 9:00 Centralising Data for Efficiency

- 15:00 Maintaining Human-Centred Client Relationships

- 21:00 Future of Technology in Client Services

- 27:00 Takeaways for CTOs and CISOs

Teamwork.com is an AI-powered project and resource management platform designed to keep client projects running smoothly, simplify resource planning, and help businesses protect their margins.

Headquartered in Cork, Ireland, Teamwork.com employs more than 200 people worldwide, with additional hubs in Denver, Gdańsk, and Belfast. The company supports teams across industries with tools that make collaboration clearer, workflows more efficient, and delivery more predictable.

With the rapid evolution of Generative AI, customer experience (CX) is evolving rapidly, too. In a recent episode of the Tech Transformed podcast, Mike Gozzo, Chief Product and Technology Officer at Ada, sat down with host Christina Stathopoulos, Founder of Dare to Data. They talked about how generative AI is changing business-to-customer interactions.

“I view it not just as a business opportunity, but we are here to solve a problem that has existed as long as commerce has,” Gozzo said. He emphasised that AI's goal isn’t just efficiency. It is about building trust and clearly understanding customer needs to allow productive interactions.

Artificial intelligence, he noted, “has really enabled what used to be much more costly to happen at scale.” The Ada Chief Product and Technology Officer pointed out that the best customer experiences are highly personalised. Comparing it to arriving at a luxury hotel where the staff already knows your name, even on your first visit. He noted that modern AI aims to make such experiences, which were once only for a select few, common for everyone.

Looking to the future, Gozzo tells Stathopoulos he believes generative AI will foster more engagement between customers and brands. “If I consider the trend, I think we will have much more natural, personalised, and effortless interactions than ever before because of this technology.”

When discussing operational challenges, especially regarding customer data management, the guest speaker stressed quality over quantity. Gozzo explained that in most AI set-ups, “the real value lies not in the data you’ve collected, but in the understanding of how your business runs, operates, and the people doing the tasks you want to automate.”

Beyond personalisation, AI should be implemented responsibly and monitored closely. “The first thing with any AI deployment is to avoid thinking of it as software you buy, deploy, and forget. They need ongoing monitoring, engagement, and maintenance,” Gozzo tells Stathopoulos. 

He suggested thorough testing processes and collaboration with specialised companies like AIUC, which verify AI systems against common risks. “These tests need to happen quarterly or yearly because the underlying models change so rapidly,” he added.

In addition to regularly conducting AI checks, the human element is also critical. AI might automate up to 80% of routine tasks, but humans will still play a vital role. Gozzo described the human role as that of an orchestrator, managing teams that include both humans and AI systems and effectively delegating tasks between them.

Finally, Gozzo talked about AI's immediate impact on customer experience. “Our leading customers’ AI agents are outperforming humans. They deliver higher-quality customer service experiences, and customers prefer interacting with their AI.” The key measure, he said, is the positive effect on business growth and customer lifetime value.

The chief technology officer’s parting advice to IT decision makers is: “The people on your team know how to make AI work. Capture their insights. Don’t treat this as a technology project. The technologist will not dominate the next decade. This is about business leaders and experts doing the heavy lifting.”

At the core of generative and agentic AI, Gozzo reminded listeners, are humans—the operational leaders and domain experts who embed knowledge into AI systems and drive real change.

• AI can transform customer experience from reactive to proactive.
• Quality data is more important than large data sets.
• Human insight is crucial for effective AI implementation.
• Governance and continuous monitoring are essential for AI systems.
• Collaboration between humans and AI is the future of work.
• The role of human agents will shift from execution to management and delegation.
• The most mature companies are moving beyond metrics like CSAT to measure AI’s impact of outcomes like customer lifetime value and retention.
• Testing and validation processes are key to successful AI deployment.
• AI can and is delivering better-than-human customer service.
• The success of AI initiatives relies on operational leaders' insights.

• 00:00 Transforming Customer Experience with AI
• 02:51 The Role of Data in AI Solutions
• 05:48 Governance and Security in AI Deployment
• 09:03 Human-AI Collaboration in Customer Service
• 12:01 The Future of AI in Customer Experience

Ada is the trusted AI-native customer service company, built to transform how enterprises engage with customers. Powered by the Ada ACX operating model—which unifies technology, methodology, and expertise—Ada deploys high-performing AI agents that deliver personalized, efficient interactions across every channel and language. 

Since 2016, Ada has powered over 5.5 billion interactions for leading brands like Square, Peloton, Canva, and monday.com, delivering extraordinary experiences at scale through higher-quality, more efficient customer service. 

With enterprise-grade trust, security, and compliance (SOC 2, GDPR, HIPAA, AIUC-1), Ada enables organizations to reduce cost-to-serve, elevate CSAT, and confidently scale AI-powered customer experience. Learn more at ada.cx.

The era of 3G is ending. For many industrial businesses, smart infrastructure systems, remote device management, and IoT connectivity rely on networks that are now being phased out globally. The question isn’t if—but when your operations could be disrupted.

In this episode of Tech Transformed, Trisha Pillay speaks with Jana Vidis, Business Development Manager at IFB, about the worldwide 3G sunset, what it means for enterprises, and how proactive planning can prevent costly disruptions. They explore the reasons behind the transition to 4G and 5G, the impact on various industries, and the strategies organisations can implement to assess their reliance on legacy devices. 

3G networks have powered connectivity for decades, offering wide coverage and reliability. But as global operators move to 4G and 5G, maintaining 3G is no longer sustainable. Carriers are discontinuing services, and support is dwindling, leaving legacy devices vulnerable to:

• Operational downtime
• Inconsistent performance
• Increased security risks

Jana emphasises:

“Have a good understanding of what devices you have. Work with IT partners to prepare for future changes. Plan your transition and act before disruption hits.”

Jana also stressed the importance of understanding current technology deployments, planning for transitions, and future-proofing investments to avoid disruptions. The conversation highlights the need for proactive measures in adapting to technological advancements and ensuring operational continuity.

The transition is already well underway across multiple regions:

• North America: AT&T, Verizon, and T-Mobile 3G networks discontinued in February 2022; Canada’s shutdown begins in early 2025.
• Europe: Most countries, including the UK, Germany, Hungary, and Greece, will complete shutdowns by the end of 2025.
• Asia: Japan phased out 3G in 2022, Singapore in July 2024, and India plans completion by the end of 2025.
• Africa: South Africa started in July 2025; other countries are slowing the transition.
• South America: Providers like Telefonica, Entel, and Claro completed shutdowns in 2022–2023.
• Middle East: Oman started shutting down in July 2024; Zain Bahrain in Q4 2022; Kuwait, Iran, and Jordan are following.

Industrial devices still using 3G must transition now to avoid operational disruption. From smart infrastructure to remote IoT systems, legacy devices left unaddressed can cause downtime, inconsistent performance, and increased security risks.

• 3G networks are being phased out to enable 4G and 5G development.
• Businesses must assess their reliance on 3G devices before shutdowns.
• Legacy devices can cause operational disruptions if not addressed.
• Understanding current technology deployments is crucial for businesses.
• Proactive planning can mitigate risks associated with network changes.
• Investing in infrastructure now can save costs in the future.
• Collaboration with IT partners is essential for smooth transitions.
• Testing new devices before rollout is a best practice.
• The demand for faster connectivity is driving technological advancements.
• Future-proofing technology investments is key to long-term success.

00:00 The Global Shift from 3G Networks

02:48 Understanding the Impact on Businesses

05:44 Assessing Reliance on 3G Devices

08:57 Operational Changes and Disruptions

12:02 Strategies for Transitioning to New Technologies

14:55 Future-Proofing Connectivity Decisions

17:44 Key Takeaways and Final Thoughts

Jana Vidis is a cybersecurity and technology expert passionate about helping businesses protect their operations and make technology work smarter. At IFB, she delivers tailored solutions, supports clients, and drives growth through strategic business development.

A champion for women in tech, Jana leads Women in Tech Aberdeen, promoting diversity, inclusion, and empowerment in the North East of Scotland. Her expertise spans cybersecurity, disaster recovery, resilience planning, connectivity, hosted voice, and cloud solutions.

Tech leaders are often led to believe that they have “full-stack observability.” The MELT framework—metrics, events, logs, and traces—became the industry standard for visibility. However, Robert Cowart, CEO and Co-Founder of ElastiFlow, believes that this MELT framework leaves a critical gap. 

In the latest episode of the Tech Transformed podcast, host Dana Gardner, President and Principal Analyst at Interabor Solutions, sits down with Cowart to discuss network observability and its vitality in achieving full-stack observability.

The speakers discuss the limitations of legacy observability tools that focus on MELT and how this leaves a significant and dangerous blind spot. Cowart emphasises the need for teams to integrate network data enriched with application context to enhance troubleshooting and security measures. 

Cowart explains that when it comes to the MELT framework, meaning “metrics, events, logs, and traces, think about the things that are being monitored or observed with that information. This is alluded to servers and applications.

“Organisations need to understand their compute infrastructure and the applications they are running on. All of those servers are connected to networks, and those applications communicate over the networks, and users consume those services again over the network,” he added.

“What we see among our growing customer base is that there's a real gap in the full-stack story that has been told in the market for the last 10 years, and that is the network.”

The lack of insights results in a constant blind spot that delays problem-solving, hides user-experience issues, and leaves organizations vulnerable to security threats. Cowart notes that while performance monitoring tools can identify when an application call to a database is slow, they often don’t explain why.

“Was the database slow, or was the network path between them rerouted and causing delays?” he questions. “If you don’t see the network, you can’t find the root cause.”

The outcome is longer troubleshooting cycles, isolated operations teams, and an expensive “blame game” among DevOps, NetOps, and SecOps.

Elastiflow’s approaches it differently. They focus on observability to network connectivity—understanding who is communicating with whom and how that communication behaves. This data not only speeds up performance insights but also acts as a “motion detector” within the organization. 

Monitoring east-west, north-south, and cloud VPC flow logs helps organizations spot unusual patterns that indicate internal threats or compromised systems used for launching external attacks.

“Security teams are often good at defending the perimeter,” Cowart says. “But once something gets inside, visibility fades. Connectivity data fills that gap.”

Cowart believes that observability can’t just be about green lights and red lights, or whether a switch is on or off. It has to stress the experience, not only the user’s but also how one application interacts with another.

“The biggest shift organizations need to make,” the ElastiFlow CEO advises, “is to move from infrastructure health to usage experience. What really counts is whether the service being delivered is performing as it should.”

A unified experience can be achieved if leaders disband conventional barriers between teams. Imagine a Venn diagram where DevOps, NetOps, and SecOps overlap—each relying on network data as a common source of truth. The network becomes the central point that connects performance, reliability, and security around shared goals as per Cowart. 

This philosophy supports ElastiFlow’s latest innovation – integrating network flow data directly into OpenTelemetry traces. Rather than treating network analytics as a separate console, ElastiFlow now includes connectivity information as part of the same trace that developers and SREs use to monitor applications.

“It’s the first time anyone has done this,” Cowart points out. “An engineer can now see within their observability platform not just an HTTP call but also the related network session, latency, and context—all in one view.”

By embedding the “fifth pillar” of observability—network flows— ElastiFlow aims to prepare organizations for future challenges. With Kubernetes, hybrid cloud, and microservices making the network more abstract yet increasingly essential, visibility at this level is no longer optional; it’s strategic.

“As infrastructure becomes self-healing and redundant,” Cowart concludes, “leaders need to concentrate less on isolated device health and more on the experience being delivered. That’s where the business value lies, and that’s where the network finally becomes clear.”

• Legacy observability tools often overlook network connectivity.
• The MELT framework leaves a critical gap – network flow data.
• Network blind spots lead to extended troubleshooting cycles.
• XOps (NetOps, DevOps, SecOps) data siloshinder effective problem resolution.
• Security threats exploit the lack of network visibility, especially in east-west traffic.
• Network flow data is essential for comprehensive observability.
• Integrating application context enhances network data utility.
• Collaboration between IT teams is necessary for effective observability.

• 00:00 Introduction to Network Observability
• 06:04 The Blind Spot in Traditional Observability
• 12:08 Security Implications of Network Observability
• 18:03 Integrating Network Data with Application Context
• 24:08 Future of Comprehensive Observability

Enterprises are discovering that the first wave of cloud adoption didn’t simplify operations. It created flexibility, but it also introduced fragmentation, rising costs, and skills gaps that now make AI adoption harder to manage. 

In this episode of Tech Transformed, analyst and host Dana Gardner speaks with two leaders from across the IBM portfolio: Maria Bracho, CTO for the Americas at Red Hat, and Tyler Lynch, Field CTO for the HashiCorp product suite. 

They discuss how organisations can move from scattered cloud operations to a unified, automated model that supports AI securely and at scale. The conversation covers the pressures leaders face today, the role of automation, and the skills and operating model changes required as AI becomes core to enterprise strategy. 

What you’ll learn 

• Why tool sprawl and shrinking teams are increasing operational risk 
• How AI amplifies gaps in data, security, and processes 
• What skills and operating model changes CIOs must prioritise 
• Why hybrid cloud is essential for multi-model AI workloads 
• The growing importance of automation in cloud and AI delivery 
• How poor data hygiene can rapidly increase AI costs 
• Practical steps for building secure, reliable AI operations 

Key insights from the discussion 

Cloud complexity is accelerating 

Most organisations now run “a sprawl of tool sets and environments,” Bracho notes, often without the people or standardized processes to manage them. While cloud created opportunities, the operational overhead has increased. 

AI raises the stakes 

Training, tuning, and inference often run in different environments, each with separate performance and security requirements. Bracho describes AI as “the killer workload,” reinforcing the need for robust hybrid architectures. 

Skills gaps slow progress 

Lynch highlights the disconnect between AI teams and production engineering teams. Without alignment, model deployment becomes slow and risky — echoing findings from the HashiCorp 2025 Cloud Complexity Report, where most organizations say platform and security teams are not working in sync. 

AI exposes underlying weaknesses 

“AI is not going to solve complexity; it will amplify what you already have,” Bracho says. But with structured processes and automation, AI can reduce operator workload and help teams adopt best practices faster. 

Automation is becoming essential 

The Cloud Complexity Report shows that more than half of enterprises see automation as key to unlocking cloud innovation. With the foundations already laid, AI can accelerate progress by improving consistency and reducing manual effort. 

Modernization is continuous 

Both guests emphasise that AI success depends on long-term investment in people, operating rhythms, and security. Consulting can help organizations start strong, but lasting results come from internal alignment and disciplined execution. 

Episode chapters 

00:00 Navigating cloud complexity

08:11 Skills and operating model challenges

15:13 Automation for cloud and AI productivity

21:48 How consulting accelerates AI readiness

24:10 Final guidance for CIOs 

About the guests 

Maria Bracho 

CTO, Americas at Red Hat. Maria advises enterprise customers on hybrid cloud architectures, platform strategy, and AI-enabled operations across large-scale environments.

Connect on LinkedIn 

Tyler Lynch 

Field CTO for the HashiCorp product suite within the IBM portfolio. Tyler works with organisations to modernise infrastructure, security, and automation practices that support cloud and AI workloads. 

Connect on LinkedIn 

About HashiCorp 

HashiCorp is the Infrastructure Cloud company, helping organisations automate hybrid and multicloud environments with Infrastructure Lifecycle Management and Security Lifecycle Management. Offerings include managed services on the HashiCorp Cloud Platform (HCP), self-hosted enterprise products, and source-available tools.

Visit hashicorp.com 

About Red Hat 

Red Hat delivers enterprise open-source solutions, including Red Hat OpenShift and Red Hat Enterprise Linux, enabling organisations to build and manage hybrid cloud environments with speed, reliability, and consistency.

Visit redhat.com 

About IBM 

IBM provides hybrid cloud, AI, and consulting expertise that brings together Red Hat, HashiCorp, and other portfolio capabilities. IBM helps organisations modernise applications, streamline operations, strengthen security, and prepare for AI at scale.

Vist ibm.com 

The semiconductor industry is at an inflection point. As systems become more intelligent, connected, and software-defined, chip design is growing too complex for humans alone. Advances in electronic design automation are reshaping how silicon is built and verified, enabling faster, smarter, and more reliable innovation from data centers to edge devices.

In the latest episode of Tech Transformed, host John Santaferraro speaks with Dr. Thomas Andersen, Vice President of AI and Silicon Innovation at Synopsys, about the real-world impact of AI in chip design. Together, they explore how AI and automation are redefining EDA, how generative AI is accelerating design efficiency, and what the Synopsys acquisition of Ansys means for the future of simulation and system-level integration.

As Dr. Andersen explains, “AI is transforming EDA. Synopsys leads in silicon design, and the Ansys acquisition expands our capabilities across multiphysics simulation and system optimization.”

The integration of complex hardware and software has become one of the greatest challenges in semiconductor and OEM innovation. Traditional sequential development, where software waits for hardware, often causes delays and missed targets. Advances in EDA tools and virtual prototyping now enable engineers to initiate software design months before silicon is finalised, thereby accelerating bring-up and enhancing collaboration across the supply chain.

“Generative AI enables more efficient design,” says Andersen. “AI reshapes engineering workflows, but human expertise remains essential.”

The result is faster time-to-market, enhanced design verification, and greater overall system reliability.

Listen to the full conversation on the Tech Transformed podcast to discover how Synopsys is advancing electronic design automation, improving engineering workflows and chip design from silicon to systems.

For more insights follow Synopsys:

• X: @Synopsys
• Instagram: @synopsyslife
• Facebook: https://www.facebook.com/Synopsys/
• LinkedIn: https://www.linkedin.com/company/synopsys/

• AI is transforming EDA and chip design by automating complex processes.
• Synopsys is a leader in silicon-to-systems design, providing critical software for chipmakers.
• The acquisition of Ansys expands Synopsys' capabilities beyond EDA.
• Generative AI is enabling more efficient and adaptable chip design.
• AI-powered observability is reshaping engineering workflows.
• The complexity of chip design has increased, requiring advanced tools and automation.
• Human expertise remains essential in chip design, despite advances in automation.
• EDA tools simulate chip designs, reducing the need for physical prototypes.
• Agent engineers are the future, automating tasks traditionally done by humans.
• The integration of AI in EDA is making the process faster, smarter, and more collaborative.

• 00:00:00 Introduction to EDA and AI
• 00:03:00 Synopsys and Its Role in Chip Design
• 00:09:00 The Impact of AI on EDA
• 00:15:00 Generative AI and Automation
• 00:21:00 Ansys Acquisition and Future Prospects

Synopsys, Inc. (Nasdaq: SNPS) is the leader in engineering solutions from silicon to systems, enabling customers to rapidly innovate AI-powered products. We deliver industry-leading silicon design, IP, simulation and analysis solutions, and design services. We partner closely with our customers across a wide range of industries to maximize their R&D capability and productivity, powering innovation today that ignites the ingenuity of tomorrow. Learn more at www.synopsys.com.

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As enterprises push to deliver software faster and more efficiently, continuous integration and continuous delivery (CI/CD) pipelines have become central to modern engineering. With increasing complexity in builds, tools, and environments, the challenge is no longer just speed, but it’s also about maintaining flow, consistency, and confidence in every release.

In this episode of Tech Transformed, host Dana Gardner joins Arpad Kun, VP of Engineering and Infrastructure at Bitrise, to explore how solid CI/CD foundations can drive innovation and enable enterprises to harness AI in more practical, impactful ways. Drawing on findings from the Bitrise Mobile DevOps Insights Report, Kun shares how teams are optimising mobile delivery pipelines to accelerate development and support intelligent automation at scale.

“Continuous integration pipelines are becoming more complex,” says Kun. “Build times are decreasing despite increasing complexity.” Faster compute and caching solutions are helping offset these pressures, but only when integrated into a cohesive CI/CD platform that can handle the rising demands of modern software delivery.

A mature CI/CD environment creates stability and predictability. When developers trust their pipelines, they iterate faster and with less friction. As Kun notes, “A robust CI/CD platform reduces anxiety around releases.” Frequent, smaller iterations deliver faster feedback, shorten release cycles, and often improve app ratings—especially in the fast-paced world of mobile and cross-platform development.

It’s easy to become swept up in the potential of AI without considering whether existing foundations can support it. Many development environments are not yet equipped to handle the iterative, data-intensive nature of AI-powered software engineering. Without scalable CI/CD pipelines, teams risk encountering bottlenecks that can cancel out the potential benefits of AI.

To truly drive innovation, enterprises must align their AI ambitions with robust automation, strong observability, and disciplined engineering practices. A well-designed CI/CD platform allows teams to integrate AI responsibly, accelerating testing, improving deployment accuracy, and maintaining agility even as complexity grows.

• Continuous integration pipelines are becoming more complex.
• Build times are decreasing despite increasing complexity.
• Faster computing and caching are key to improving delivery speed.
• Flaky tests have increased significantly, causing inefficiencies.
• Monitoring and isolating flaky tests can improve build success rates.
• Maintaining flow for engineers is crucial for productivity.
• A robust CI/CD platform reduces anxiety around releases.
• Frequent iterations lead to faster feedback and improved app ratings.
• Cross-platform development is on the rise, especially with React Native.
• The future of software development will be influenced by AI.

For more insights, follow Bitrise:

X: @bitrise

Instagram: @bitrise.io

Facebook: facebook.com/bitrise.io

LinkedIn: linkedin.com/company/bitrise

00:00 Introduction to Tech Transformed Podcast

01:08 The Complexity of Continuous Integration

04:24 Challenges of Increased Speed in Development

08:09 The Importance of Continuous Integration and Deployment

10:32 Building a Robust CI/CD Platform

14:25 The Impact of Release Frequency on Business

16:52 Types of Applications and Development Trends

18:47 Aligning Development with Business Goals

24:15 Preparing for the Future of Software Development

Bitrise is a top mobile CI/CD platform, streamlining build, test, and deployment for mobile apps. It offers a user-friendly interface, robust integrations, and scalable infrastructure to simplify development and ensure efficient delivery of high-quality apps.

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Website: www.em360tech.com

X: @EM360Tech

LinkedIn: EM360Tech